高密度乳腺是一种已知的乳腺癌风险因素。本研究旨在开发和调整两个深度卷积神经网络(MLO、CC)用于在合成的2D断层摄影成像中自动分类乳腺密度。共有4605张合成的2D图像(1665名患者，年龄：57±37岁)按照ACR(American College of Radiology)密度(A-D)进行了标记。两个具有11个卷积层和3个完全连接层的DCNN，用70%的数据进行了训练，而20%的数据进行了验证。剩余的10%用作单独的测试数据集，包括460张图像(380名患者)。所有测试数据集中的乳腺X线照片都由两名放射科医生(一名有两年经验，另一名有11年经验)进行了盲读，并以共识作为参考标准。通过计算Cohen的kappa系数评估了读者间和读者内可靠性，评估了自动分类的诊断准确性措施。MLO和CC投影的两个模型在ACR A/B和ACR C/D区分方面的平均灵敏度为80.4%(95%-CI 72.2-86.9)，特异度为89.3%(95%-CI 85.4-92.3)，准确度为89.6%(95%-CI 88.1-90.9)。DCNN与人类专家和读者间一致性均为"充分"(Cohen的kappa系数分别为0.61和0.63)。DCNN允许基于ACR BI-RADS系统对乳房密度进行准确、标准化和观察者独立的分类。• DCNN在合成的2D断层摄影成像中的乳房密度评估方面与人类专家表现相当。• 所提出的技术可能有助于对断层摄影成像中的乳房密度进行准确、标准化和观察者独立的评估。©2023.作者。

High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions.In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11 years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen's kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated.The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2-86.9), a specificity of 89.3% (95%-CI 85.4-92.3), and an accuracy of 89.6% (95%-CI 88.1-90.9) in the differentiation between ACR A/B and ACR C/D. DCNN versus human and inter-reader agreement were both "substantial" (Cohen's kappa: 0.61 versus 0.63).The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system.• A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions. • The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis.© 2023. The Author(s).